1. Field of the Invention
The present invention relates to a method of process control such as for controlling the temperature of a glass melting furnace, which involves latency (dead time), i.e. a no-response period from a variation in the manipulated input till observation of its effect appearing as a variation in the process state.
2. Description of the Prior Art
When a stepwise manipulated input as shown in FIG. 13(a) is applied to a process involving a dead time, i.e. dead time process, a process response appears as a gradual variation after dead time L as shown in FIG. 13(b). In the PID control which does not take the dead time into account, a manipulated input varies as shown in FIG. 13(c), with a process response becoming vibrational as shown in FIG. 13(d). Such a phenomenon is called hunting which is an undesirable phenomenon well known in process control.
As will be appreciated, a dead time process is extremely difficult to control. Generally, therefore, this type of process control has been carried out by skilled operators. In recent years, a number of methods have been proposed for controlling a dead time process. The following are typical examples of such proposals.
One is the Smith method (O. J. M. Smith: Feedback Control System, McGraw-Hill Book Co., Inc., New York (1958), and O. J. M. Smith: ISA Jour., Vol. 1, No. 2 (1959)). Another is the state prediction method (Mitshuhiko Araki: "Control of Dead Time System and Nonlinear System", A Guideline to Control Engineering (Nippon Automatic Control Association), pages 139-162 (1985)). According to these methods, process characteristics are described in approximate equations, and future process behavior is predicted and controlled by referring to past manipulated inputs and observable process states.
Since process characteristics are represented by numerical expressions according to the Smith method and state prediction method as noted above, the characteristics of the process subjected to control must be grasped with high accuracy. Further, it is necessary for the process to be a linear process or a relatively simple nonlinear process which may be approximated with a plurality of linear processes.
However, the nature of an ordinary process is seldom grasped accurately although broadly recognized by experience. Besides, many processes have complex nonlinear characteristics.
Thus, the prior methods as noted above have the disadvantage of being hardly applicable to ordinary processes having nonlinear characteristics although useful in controlling special processes having linear characteristics.